A managerial community of Web Services for management of communities of Web Services
Bibliographic record
Abstract
Nowadays, Web services are considered as new and attracting distributed approach of application/services integration over the Internet. As the number of Web Services is exponentially growing and expected to do so for the next decade, the need for categorizing and/or classifying Web Services is very crucial for their success and the success of the underlying SOA. Categorization aims at systematizing Web Services according to their functionalities and their Quality of Service attributes. Communities of Web Services have been used to gather Web Services based on their functionalities. In fact, Web Services in a community usually offer similar and/or complementary services. In this paper, we augment Web Services communities' classification by adding a new support layer for Quality of Service classification. This is done through Quality of Services specification, monitoring, and certification of different Web Services. A Web Service might be admitted to a community thanks to its high Quality of Service or might be ejected from a community due to its low Quality of Service. We propose a managerial community of Web Services that is able to monitor and certify Quality of Web Services in other communities. This managerial community offers services to other communities, Web Services providers, and Web Services clients by monitoring and certifying Web Services. The focus of this paper is the use of the managerial community to select Web Services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".